Natural language processing (NLP) | Amar iSchool

Natural language processing (NLP)

Become an expert NLP Practitioner

Intermediate 0(0 Ratings) 0 Students enrolled
Created by Amir Hosain Raj Last updated Sat, 19-Jun-2021 Bengali
What will i learn?
  • You will learn practical NLP principles and processes that you can use with clients, in your personal life, career or relationships right away.
  • You will learn how to inspire change in people's limiting ideologies and empower them to live more harmonious and autonomous lives.
  • Utilise powerful language patterns for influencing and modifying behaviours in all contexts, from business to education and coaching.
  • Understand the essential applied psychological principles, tools and methodologies that underpin the masterful practice of NLP.
  • You will learn advanced NLP models of change that can act as the catalyst for instigating significant transformation in your clients.
  • You learn about Deep Learning

Curriculum for this course
0 Lessons 00:00:00 Hours
Python Text Basics
0 Lessons 00:00:00 Hours
Requirements
  • Basics of Python
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Description



Requirements 

● Basics of Python 

● Jupyter Notebook

Topic 

Subtopic



1. Python Text Basics 

1. Intro to Python text basics 

2. Working with a text file 

3. Working with pdf 

4. Regular expressions



2. What is NLP? 

1. A changing Field 

2. Resources 

3. Tools 

4. Python Libraries 

5. Example Applications 

6. Ethics Issues



3. NLP Basics 

1. Intro NLP 

2. Spacy Setup & Overview 

3. Spacy Basics 

4. Stop Words 

5. POS 

6. Phrase Matching 

7. Vocabulary 

8. NER 

9. Sentence Segmentation



4. Text Classification 

1. Intro to Text Classification 

2. ML Overview 

3. Classification Metrics 

4. Confusion Matrix 

5. Scikit-Learn 

6. Text Feature Extraction using Scikit-Learn 7. Text Classification using Scikit-Learn



5. Topic Modeling with 

NMF and SVD

1. Stop words, stemming, & lemmatization 

2. Term-document matrix 

3. Topic Frequency-Inverse Document Frequency (TF-IDF) 

4. Singular Value Decomposition (SVD) 

5. Non-negative Matrix Factorization (NMF) 6. Truncated SVD, Randomized SVD


6. Sentiment classification with Naive Bayes, Logistic regression, and ngrams 


1. Sparse matrix storage 

2. Counters 

3. The fastai library 

4. Naive Bayes 

5. Logistic Regression 

6. Ngrams

7. Logistic Regression with Naive Bayes features, with trigrams 

8. Vocabulary & Feature Extraction

9. Training 

10. Testing 

11. Cost Function 

12. Project: Positive-Negative Movie Review 13. Error Analysis




7. Deep Learning 

1. Recurrent Neural Network 

2. Long short-term memory 

3. Gated recurrent unit



8. Translation with RNNs 

1. Intro to PyTorch 

2. Review Embeddings 

3. Bleu Metric 

4. Teacher Forcing 

5. Bidirectional 

6. Attention



9. Translation with the 

Transformer architecture

1. Transformer Model 

2. Transforming Word Vectors 

3. Multi-head Attention 

4. Masking 

5. Label Smoothing 

6. Searching Documents



10. Bias & ethics in NLP 

1. Bias in word Embeddings 

2. Types of Bias 

3. Attention Economy 

4. Drowning in fraudulent/fake info 

5. PCA Algorithm




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